DOCSLIB.ORG

Tree Search Best-First Search Romania with Step Costs in Km Greedy Search

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Tree Search Best-First Search Romania with Step Costs in Km Greedy Search Best-first search Idea: use an evaluation function for each node { estimate of \desirability" Informed search algorithms ) Expand most desirable unexpanded node Implementation: fringe is a queue sorted in decreasing order of desirability Special cases: Chapter 4, Sections 1{2 greedy search A∗ search Chapter 4, Sections 1{2 1 Chapter 4, Sections 1{2 4 Outline Romania with step costs in km } Best-first search Straight−line distance Oradea 71 to Bucharest } ∗ Neamt Arad 366 A search Bucharest 0 Zerind 87 75 151 Craiova 160 } Heuristics Iasi Dobreta 242 Arad 140 Eforie 161 92 Fagaras 178 Sibiu Fagaras 99 Giurgiu 77 118 Hirsova 151 80 Vaslui Iasi 226 Rimnicu Vilcea Timisoara Lugoj 244 142 Mehadia 241 111 211 Neamt 234 Lugoj 97 Pitesti Oradea 380 70 98 Pitesti 98 146 85 Hirsova Mehadia 101 Urziceni Rimnicu Vilcea 193 Sibiu 75 138 86 253 Bucharest Timisoara 120 329 Dobreta 90 Urziceni 80 Craiova Eforie Vaslui 199 Giurgiu Zerind 374 Chapter 4, Sections 1{2 2 Chapter 4, Sections 1{2 5 Review: Tree search Greedy search function Tree-Search( problem, fringe) returns a solution, or failure Evaluation function h(n) (heuristic) fringe Insert(Make-Node(Initial-State[problem]), fringe) = estimate of cost from n to the closest goal loop do if fringe is empty then return failure E.g., hSLD(n) = straight-line distance from n to Bucharest node Remove-Front(fringe) Greedy search expands the node that appears to be closest to goal if Goal-Test[problem] applied to State(node) succeeds return node fringe InsertAll(Expand(node, problem), fringe) A strategy is defined by picking the order of node expansion Chapter 4, Sections 1{2 3 Chapter 4, Sections 1{2 6 Greedy search example Greedy search example Arad Arad 366 Sibiu Timisoara Zerind 329 374 Arad Fagaras Oradea Rimnicu Vilcea 366 380 193 Sibiu Bucharest 253 0 Chapter 4, Sections 1{2 7 Chapter 4, Sections 1{2 10 Greedy search example Properties of greedy search Complete?? Arad Sibiu Timisoara Zerind 253 329 374 Chapter 4, Sections 1{2 8 Chapter 4, Sections 1{2 11 Greedy search example Properties of greedy search Complete?? No{can get stuck in loops, e.g., with Oradea as goal, Arad Iasi ! Neamt ! Iasi ! Neamt ! Complete in finite space with repeated-state checking Sibiu Timisoara Zerind 329 374 Time?? Arad Fagaras Oradea Rimnicu Vilcea 366 176 380 193 Chapter 4, Sections 1{2 9 Chapter 4, Sections 1{2 12 Properties of greedy search A∗ search Complete?? No{can get stuck in loops, e.g., Idea: avoid expanding paths that are already expensive Iasi ! Neamt ! Iasi ! Neamt ! Evaluation function f(n) = g(n) + h(n) Complete in finite space with repeated-state checking g(n) = cost so far to reach n Time?? O(bm), but a good heuristic can give dramatic improvement h(n) = estimated cost to goal from n Space?? f(n) = estimated total cost of path through n to goal A∗ search uses an admissible heuristic i.e., h(n) ≤ h∗(n) where h∗(n) is the true cost from n. (Also require h(n) ≥ 0, so h(G) = 0 for any goal G.) E.g., hSLD(n) never overestimates the actual road distance Theorem: A∗ search is optimal Chapter 4, Sections 1{2 13 Chapter 4, Sections 1{2 16 Properties of greedy search A∗ search example Complete?? No{can get stuck in loops, e.g., Iasi ! Neamt ! Iasi ! Neamt ! Arad 366=0+366 Complete in finite space with repeated-state checking Time?? O(bm), but a good heuristic can give dramatic improvement Space?? O(bm)|keeps all nodes in memory Optimal?? Chapter 4, Sections 1{2 14 Chapter 4, Sections 1{2 17 Properties of greedy search A∗ search example Complete?? No{can get stuck in loops, e.g., Iasi ! Neamt ! Iasi ! Neamt ! Arad Complete in finite space with repeated-state checking Sibiu Timisoara Zerind m Time?? O(b ), but a good heuristic can give dramatic improvement 393=140+253 447=118+329 449=75+374 Space?? O(bm)|keeps all nodes in memory Optimal?? No Chapter 4, Sections 1{2 15 Chapter 4, Sections 1{2 18 A∗ search example A∗ search example Arad Arad Sibiu Timisoara Zerind Sibiu Timisoara Zerind 447=118+329 449=75+374 447=118+329 449=75+374 Arad Fagaras Oradea Rimnicu Vilcea Arad Fagaras Oradea Rimnicu Vilcea 646=280+366 415=239+176 671=291+380 413=220+193 646=280+366 671=291+380 Sibiu Bucharest Craiova Pitesti Sibiu 591=338+253 450=450+0 526=366+160 553=300+253 Bucharest Craiova Rimnicu Vilcea 418=418+0 615=455+160 607=414+193 Chapter 4, Sections 1{2 19 Chapter 4, Sections 1{2 22 A∗ search example Optimality of A∗ (standard proof) Suppose some suboptimal goal G2 has been generated and is in the queue. Arad Let n be an unexpanded node on a shortest path to an optimal goal G1. Start Sibiu Timisoara Zerind 447=118+329 449=75+374 n Arad Fagaras Oradea Rimnicu Vilcea 646=280+366 415=239+176 671=291+380 G G Craiova Pitesti Sibiu 2 526=366+160 417=317+100 553=300+253 f(G2) = g(G2) since h(G2) = 0 > g(G1) since G2 is suboptimal ≥ f(n) since h is admissible ∗ Since f(G2) > f(n), A will never select G2 for expansion Chapter 4, Sections 1{2 20 Chapter 4, Sections 1{2 23 A∗ search example Optimality of A∗ (more useful) Lemma: A∗ expands nodes in order of increasing f value∗ Arad Gradually adds \f-contours" of nodes (cf. breadth-first adds layers) Sibiu Timisoara Zerind Contour i has all nodes with f = fi, where fi < fi+1 447=118+329 449=75+374 O Arad Fagaras Oradea Rimnicu Vilcea N 646=280+366 671=291+380 Z I Sibiu Bucharest Craiova Pitesti Sibiu A 591=338+253 450=450+0 526=366+160 417=317+100 553=300+253 380 S F V 400 T R L P H M U B 420 D E C G Chapter 4, Sections 1{2 21 Chapter 4, Sections 1{2 24 Properties of A∗ Properties of A∗ Complete?? Complete?? Yes, unless there are infinitely many nodes with f ≤ f(G) Time?? Exponential in [relative error in h × length of soln.] Space?? Keeps all nodes in memory Optimal?? Chapter 4, Sections 1{2 25 Chapter 4, Sections 1{2 28 Properties of A∗ Properties of A∗ Complete?? Yes, unless there are infinitely many nodes with f ≤ f(G) Complete?? Yes, unless there are infinitely many nodes with f ≤ f(G) Time?? Time?? Exponential in [relative error in h × length of soln.] Space?? Keeps all nodes in memory Optimal?? Yes|cannot expand fi+1 until fi is finished A∗ expands all nodes with f(n) < C∗ A∗ expands some nodes with f(n) = C∗ A∗ expands no nodes with f(n) > C∗ Chapter 4, Sections 1{2 26 Chapter 4, Sections 1{2 29 Properties of A∗ Proof of lemma: Consistency Complete?? Yes, unless there are infinitely many nodes with f ≤ f(G) A heuristic is consistent if Time?? Exponential in [relative error in h × length of soln.] h(n) ≤ c(n; a; n0) + h(n0) n Space?? If h is consistent, we have c(n,a,n') h(n) f(n0) = g(n0) + h(n0) n' = g(n) + c(n; a; n0) + h(n0) ≥ g(n) + h(n) h(n') = f(n) G I.e., f(n) is nondecreasing along any path. Chapter 4, Sections 1{2 27 Chapter 4, Sections 1{2 30 Admissible heuristics Relaxed problems E.g., for the 8-puzzle: Admissible heuristics can be derived from the exact solution cost of a relaxed version of the problem h1(n) = number of misplaced tiles h2(n) = total Manhattan distance If the rules of the 8-puzzle are relaxed so that a tile can move anywhere, (i.e., no. of squares from desired location of each tile) then h1(n) gives the shortest solution 7 2 4 51 2 3 If the rules are relaxed so that a tile can move to any adjacent square, then h2(n) gives the shortest solution 5 6 4 5 6 Key point: the optimal solution cost of a relaxed problem 8 3 1 7 8 is no greater than the optimal solution cost of the real problem Start State Goal State h1(S) =?? h2(S) =?? Chapter 4, Sections 1{2 31 Chapter 4, Sections 1{2 34 Admissible heuristics Relaxed problems contd. E.g., for the 8-puzzle: Well-known example: travelling salesperson problem (TSP) Find the shortest tour visiting all cities exactly once h1(n) = number of misplaced tiles h2(n) = total Manhattan distance (i.e., no. of squares from desired location of each tile) 7 2 4 51 2 3 5 6 4 5 6 8 3 1 7 8 Start State Goal State Minimum spanning tree can be computed in O(n2) h1(S) =?? 6 and is a lower bound on the shortest (open) tour h2(S) =?? 4+0+3+3+1+0+2+1 = 14 Chapter 4, Sections 1{2 32 Chapter 4, Sections 1{2 35 Dominance Summary If h2(n) ≥ h1(n) for all n (both admissible) Heuristic functions estimate costs of shortest paths then h2 dominates h1 and is better for search Good heuristics can dramatically reduce search cost Typical search costs: Greedy best-first search expands lowest h d = 14 IDS = 3,473,941 nodes { incomplete and not always optimal ∗ A (h1) = 539 nodes ∗ ∗ A (h2) = 113 nodes A search expands lowest g + h d = 24 IDS ≈ 54,000,000,000 nodes { complete and optimal ∗ A (h1) = 39,135 nodes { also optimally efficient (up to tie-breaks, for forward search) ∗ A (h2) = 1,641 nodes Admissible heuristics can be derived from exact solution of relaxed problems Given any admissible heuristics ha, hb, h(n) = max(ha(n); hb(n)) is also admissible and dominates ha, hb Chapter 4, Sections 1{2 33 Chapter 4, Sections 1{2 36.
Load more

Recommended publications
  • Economy of Sibiu County. Resources for a Future Development
    Revista Economică 67:5 (2015) ECONOMY OF SIBIU COUNTY. RESOURCES FOR A FUTURE DEVELOPMENT. POPESCU Doris-Louise1 Lucian Blaga University of Sibiu, Romania Abstract: Economically, the County of Sibiu has been characterized, especially after 2007, by an accelerated speed of development, the recorded increase pushing our County among the most dynamic economies at regional and national level as well. The present paper aims at analyzing the specificity of the economic development of Sibiu County, namely to identify the resources of the obtained economic progress. The purpose of this study also consists in identifying new opportunities for the local economy, outlining new sources of development that are more important as competition, both at regional and national level, is tighter and tighter. Keywords: economic development, employment, industry. JEL classification: N34, N64, N74, N94, O14. 1. The County of Sibiu. Population and Labor Force. According to official data, the County of Sibiu records a total surface of 5.432 km², being composed, from the administrative point of view, of 2 municipalities, 9 towns, 23 communes and 162 villages. The population of the County of Sibiu numbers 397.322 inhabitants, 66.15 % of them living in urban areas, and 33.85 % in rural areas (Statistical Yearbook of Romania 2013/Population and Housing Census 2011). Taking into consideration this indicator, the County of Sibiu presents a level of urbanization above average, 1Assist. Prof., PhD, "Lucian Blaga" University of Sibiu, Faculty of Economic Sciences Department of Management, Marketing and Business Administration, [email protected] 139 Revista Economică 67:5 (2015) population distribution, at national level, showing a percentage of 54 % of urban population, as compared to 46 % rural population.
    [Show full text]
  • LIV CICA – XV SECURITY FORUM KRAKOW 2020 7Th–8Th October 2020
    LIV CICA – XV SECURITY FORUM KRAKOW 2020 7th–8th October 2020 & 15th ANNIVERSARY OF WSBPI “APEIRON” Aparthotel Vanilla Bobrzyńskiego 33 Street 30-348 Kraków, Poland aparthotelvanilla.pl/en/ Main organisers CICA International University of Public and Individual Security “Apeiron” in Krakow Co-organisers Nebrija University in Madrid, Spain Institute for National and International Security (INIS), Serbia Institute of Security and Management, Pomeranian University in Slupsk, Poland University of Security Management in Košice, Slovakia “Nicolae Bălcescu” Land Forces Academy in Sibiu, Romania Armed Forces Academy of General Milan Rastislav Štefánik in Liptovsky Mikulas, Slovakia Autonomous University of Lisbon, Portugal Matej Bel University in Banská Bystrica, Slovakia Lviv University of Business and Law, Ukraine The College of Regional Development and Banking Institute – AMBIS, Brno, Czech Republic Military University of Technology in Warsaw, Poland Department of the Sociology of Dispositional Groups, Institute of Sociology, Wroclaw University, Poland Conference date and venue 7th–8th October 2020 Aparthotel Vanilla Bobrzyńskiego 33 Street 30-348 Kraków, Poland +48 12 354 01 50 https://aparthotelvanilla.pl/en/ We invite to the conference: scholars and experts studying security officers and employees of institutions and services ensuring civic security students and Ph.D. candidates Proposed topics Europe’s chances, challenges, risks and threats security and defence policy in the actions of the EU and NATO international alliances and conflicts
    [Show full text]
  • Sustainable Development Goals: How to Ensure Inclusive and Equitable Quality Education and Promote Lifelong Learning Opportuniti
    Sustainable Development Goals: How to Ensure Inclusive and Equitable Quality Education and Promote Lifelong Learning Opportunities for All Ramada Hotel, Sibiu, Romania May 11-12, 2017 organized by the UNESCO Chair in Quality Management of Higher Education and Lifelong Learning of the “Lucian Blaga” University of Sibiu with the support of the Ministry of National Education The Sustainable Development Goals conference social program has three elements: 1. Suggested Dinner Places: Bistro Capsicum Situated at the very heart of Sibiu's historical district, overlooking the Astra Park and the County Library, Capsicum is the perfect venue for tourists and locals alike who seek to enjoy fresh, high-quality food at reasonable prices, in a cozy and friendly atmosphere. Learn more: Facebook Route plan: Directions Google+ TripAdvisor Conference Day 1, Thursday, May 11, 2017 2. Networking Dinner (18:00-20:00): Cămara Boierului at the Hilton Hotel The Sibiu Hilton Hotel is located in a greenery and peaceful scenery, at the edge of the Dumbrava forest, 4 km from downtown and only 50m from the open–air Astra National Museum of Traditional Folk Art. Cămara Boierului at the Hilton Hotel provides a warm and welcoming environment for the tourists to enjoy authentic Romanian specialty cuisine, featuring a combination of unique and delicious flavours and dishes from Transylvania, in a traditional atmosphere. Learn more: Hilton Route plan: Directions Facebook TripAdvisor Conference Day 2, Friday 12, 2017 3. A Walking Tour of Sibiu: Sibiu (‘Hermannstadt’ in German) was the largest and wealthiest of the seven walled citadels built in the 12th century by the German settlers known as ‘Transylvanian Saxons’.
    [Show full text]
  • A Network Analysis of Sibiu County, Romania ⇑ Cristina-Nicol Grama A,1, Rodolfo Baggio B, a University of Applied Sciences, Austria B Bocconi University, Italy
    Research Notes / Annals of Tourism Research 47 (2014) 77–95 89 A network analysis of Sibiu County, Romania ⇑ Cristina-Nicol Grama a,1, Rodolfo Baggio b, a University of Applied Sciences, Austria b Bocconi University, Italy Introduction Modern network analysis methods are increasingly used in tourism studies and have shown to be able to provide scholars and practitioners with interesting outcomes. Nonetheless, the availability of investigations conducted at a broad scale on tourism destinations is still limited thus restraining our ability to understand the mechanisms that underlie the formation and the evolution of these complex adaptive systems. With this research note we aim at contributing to the field by augmenting the cat- alogue of tourism destination network studies and present the preliminary results of an investigation conducted in the county of Sibiu, a renowned Romanian destination. The data Sibiu county lies in the heart of Romania (270 km from Bucharest) in the historical region of Tran- sylvania. In 2007, Sibiu has been the European Capital of Culture (together with Luxembourg). The destination accounts for roughly 250 000 arrivals and 460 000 overnight stays. Sibiu has a manage- ment organisation (AJTS) which is a public-private partnership in charge of promoting and marketing the county as a destination, and working in close collaboration with the local government. The tourism infrastructure is well developed and counts about 500 establishments providing more than 6000 rooms (all data and a thorough description in Richards & Rotariu, 2011). The data for the network analysis were collected by using a number of publicly available docu- ments (see Baggio, Scott, & Cooper, 2010 for details) complemented by a survey conducted on 551 operators (179 questionnaires were returned) aimed at validating the data collected and evaluating type and intensity of the relationships.
    [Show full text]
  • From Meseberg to Sibiu Four Paths to European ‚Weltpolitikfähigkeit’
    POLICY PAPER 15 NOVEMBER 2018 #WELTPOLITIKFÄHIGKEIT #MESEBERG #CFSP FROM MESEBERG TO SIBIU FOUR PATHS TO EUROPEAN ‚WELTPOLITIKFÄHIGKEIT’ Executive summary ▪ NICOLE KOENIG The EU has to become more ‘weltpolitikfähig’: it has to be able to play a role, as a Union, in Deputy Director, shaping global affairs. This was one of the key messages of Juncker’s 2018 State of the Union Jacques Delors Institute speech as well as the Franco-German Meseberg Declaration. The question is of course: how? Berlin This new label cannot conceal the fact that the EU’s Weltpolitikfähigkeit is confronted by a trilem- ma of three partly conflicting objectives: internal legitimacy, efficiency/speed, and effectiveness. This policy paper explores four paths towards greater Weltpolitikfähigkeit proposed by the Meseberg Declaration: It questions to what extent Paris and Berlin are truly on the same page and how these proposals interact with the trilemma identified above. 1. An extension of qualified majority voting The extension of qualified majority voting to sub-areas (passerelle clause) or selected deci- sions (enabling clause) within the realm of the Common Foreign and Security Policy aims at enhancing the EU’s efficiency and effectiveness. However, the debate is likely to run into political blockades, as member states will point to a lack of legitimacy and cling on to their national sovereignty. 2. An EU Security Council There is general Franco-German agreement on such a format but divergence on the details. There could be a less ambitious reform whereby the European Council would meet yearly in the format of a European Security Council. Its impact on efficiency and effectiveness would, however, be limited.
    [Show full text]
  • High-Performance Green Port Giurgiu
    TEN-T Multi-Annual Programme High-performance Green Port Giurgiu Member States involved: 2012-EU-18089-S Romania Part of Priority Project 18 Implementation schedule Start date: July 2013 End date: August 2015 EU contribution: €326,505 Additional information: Coordinator’s Report of the Priority Project: http://ec.europa.eu/transport/themes/ infrastructure/ten-t-policy/priority- projects/european- coordinators_en.htm European Commission, DG MOVE http://ec.europa.eu/transport/ index_en.html Innovation and Networks Executive This Action addresses Priority Project Nr. 18 “Waterway axis Rhine/Meuse- Agency (INEA) Main-Danube” of the TEN T network. The Action aims at the development https://ec.europa.eu/inea of an eco-efficient Danube engineering and operation port system, in line with the principles of the “Joint Statement on Guiding Principles for Beneficiaries and Implementing Development of Inland Navigation in the Danube River Basin” (ICPDR, bodies: Danube Commission, Sava Commission, 2008). ILR Logistica Romania S.R.L. www.ilr.com.ro The overall objective of the Action is therefore to turn the port of Giurgiu into the first “Green Danube Port” based on integrated energy-efficiency Industrie-Logistik-Linz GmbH & Co KG concepts and comprehensive environmental measures for intermodal ports. www.ill.co.at It will first take stock of the current situation by undertaking a technical and operational analysis, a market analysis and an environmental S.C. Administratia Zonei Libere Giurgiu assessment. Then the Action will develop concepts that will support the S.A introduction of innovative technology in the port through a series of www.zlg.ro studies. The Action will eventually design the new port and define its business model.
    [Show full text]
  • Overview of the 85 VAP Visits in the Period 1998-2010, Funded by the W.K
    Overview of the 85 VAP visits in the period 1998-2010, funded by the W.K. Kellog Foundation (WKKF) and the Carnegie Corporation (CC) 85. The National University of Kyiv-Mohyla Academy, Kyiv, Ukraine, June 7 to 11, 2010 (FSU) (CC) Team Members: John Davies (Team Leader), formerly Pro Vice Chancellor (Research and Enterprise) at Anglia Ruskin University, United Kingdom Robin Farquhar, formerly President of The University of Winnipeg and also of Carleton University, Canada Hans de Wit, Professor (lector) of Internationalisation of Higher Education at the School of Economics and Management of the Hogeschool van Amsterdam, University of Applied Sciences and formerly Vice President (International) University of Amsterdam John Lotherington, Vice-President, Program Operations, Salzburg Global Seminar. 84. “Metekhi” Public University, Tbilisi, Georgia, October 18 to 23, 2008 (FSU) (CC) Team Members: Istvan Teplan (Team Leader), Director-General, Hungarian Government Centre for Public Administration and Human Resource Services; former Senior Vice President, The Central European University, Budapest, Hungary Andrea Dee Harris, President, East-West Executive Resource Group (EWER Group), Consulting; former Regional Vice President for the South Caucasus, The Eurasia Foundation, Washington DC, USA Tapio Markkanen, Professor, University of Helsinki; former Secretary General, Finnish Council of University Rectors, Helsinki, Finland Helene Kamensky, Program Director, Salzburg Global Seminar, Austria 83. Belarus State University, Minsk, Belarus, June 24 to 29, 2008 (FSU) (CC) Team Members: Ramadhikari Kumar, Rector, Jawaharlal Nehru University, New Delhi, India (Team Leader) Eva Egron-Polak, Secretary General and Executive Director, International Association of Universities (IAU), Paris, France Jürgen Schreiber, Area Manager for Ecology, Geology and Life Science Business Unit, Fraunhofer Institute for Non-Destructive Evaluation for Quality and Safety, Dresden, Germany Helene Kamensky, Program Director, Salzburg Global Seminar, Austria 82.
    [Show full text]
  • Problem Solving by Search 2
    Problem Solving by Search 2 Uwe Egly Vienna University of Technology Institute of Information Systems Knowledge-Based Systems Group Outline Introduction Heuristic Search Greedy Search A∗-search Admissible Heuristics Dominance Relaxed Problems Summary Overview Search: very important technique in CS and AI Different kinds of search: ◮ Deterministic search ◮ Uninformed (“blind”) search strategies ✔ ◮ Informed or heuristic search strategies: use information about problem structure ◮ Local search ◮ Search in game trees (not covered in this course) In this lecture: Heuristic search Basic Ideas of Heuristic Search ◮ Use problem- or domain-specific knowledge during search ◮ Implemented by a heuristic function h “desirability” (expand “most-desired” node next (= a kind of best-first search)) ◮ Evaluation function f (n): estimation for a function f ∗(n) ◮ h(n): estimates minimal costs from state n to a goal state (h(n) = 0 always holds for a goal state) ◮ Computation of h(n) must be easy ◮ Consider two algorithms: ◮ greedy search and ∗ ◮ A search Romania Again Straight−line distance Oradea 71 to Bucharest Neamt Arad 366 Bucharest 0 Zerind 87 75 151 Craiova 160 Iasi Dobreta 242 Arad 140 Eforie 161 92 Fagaras 178 Sibiu Fagaras 99 Giurgiu 77 118 Hirsova 151 80 Vaslui Iasi 226 Rimnicu Vilcea Timisoara Lugoj 244 142 Mehadia 241 111 211 Neamt 234 Lugoj 97 Pitesti Oradea 380 70 98 Pitesti 98 146 85 Hirsova Mehadia 101 Urziceni Rimnicu Vilcea 193 Sibiu 75 138 86 253 Bucharest Timisoara 120 329 Dobreta 90 Urziceni 80 Craiova Eforie Vaslui 199 Giurgiu Zerind
    [Show full text]
  • CAP I+II + III/840-Engl
    ANTIQUE OTTOMAN RUGS IN TRANSYLVANIA dedicated to Emil Schmutzler Copyright © 2005 Stefano Ionescu www.transylvanianrugs.com mail: [email protected] Published in association with ESSELIBRI Via Ferdinando Russo, 33d - Naples www.simone.it - mail: [email protected] VERDUCI EDITORE Via Gregorio VII, 186 - Rome www.verduci.it - [email protected] Printed in Italy ISBN 88-7620-705-0 All rights reserved. No part of this publication may be reproduced in any form without permission in writing from the owner of the copyright. ANTIQUE OTTOMAN RUGS IN TRANSYLVANIA published by STEFANO IONESCU Rome, 2005 with the patronage of THE SUPERIOR CONSISTORY OF THE EVANGELICAL CHURCH A.C. OF ROMANIA, SIBIU THE MINISTRY OF CULTURE AND RELIGIOUS AFFAIRS OF ROMANIA State Bureau for National Cultural Patrimony NATIONAL BRUKENTHAL MUSEUM, SIBIU ROMANIAN EMBASSY TO THE HOLY SEE, ROME ACADEMY OF ROMANIA IN ROME and with the support of the MARSHALL AND MARILYN R. WOLF FOUNDATION, NEW YORK RAÕIU FAMILY FOUNDATION, UNITED KINGDOM 260 Ottoman Rugs of the 15th-18th Centuries In the Protestant Churches of Transylvania and the Museums of Romania Saxon Evangelical C.A. Church Collections BRAÃOV - KRONSTADT All rugs in the collection of the BLACK CHURCH and a selection from BLUMENAU, ÃCHEI and BARTHOLOMEW CHURCHES BIERTAN - BIRTHÄLM HÅRMAN - HONIGBERG MEDIAÃ - MEDIASCH SEBEÃ - MÜHLBACH SIGHIÃOARA - SCHÄßBURG AGÂRBICIU - ARBEGEN, AGNITA - AGNETHELN, BÅGACIU - BOGESCHDORF BÅLCACIU - BULKESCH, BISTRIÕA - BISTRITZ, BOD - BRENNDORF, CISNÅDIE - HELTAU CINCU - GROßSCHENK, DEALUL
    [Show full text]
  • Ioana Giurgiu Cognitive Computing and Industry Solutions IBM Research - Zurich Curriculum Vitae Saumerstr
    Address: AI for Services and Industry Research Ioana Giurgiu Cognitive Computing and Industry Solutions IBM Research - Zurich Curriculum Vitae Saumerstr. 4, CH-8803 Rueschlikon, Switzerland Email: [email protected] Phone: +41 78 748 90 70 March 2019 Nationality: Romanian Permit: B Research Interests I am a research scientist in Artificial Intelligence and Machine Learning. My current research spans over several intersecting AI directions: explainable and robust AI, multi-task learning, anomaly detection and predictive analytics based on multivariate sensor temporal data. In all lines of work, my main focus is on deep learning methods. Depending on the use cases, in addition to a deep knowledge of AI and ML, domain knowledge – such as IT systems management – is essential. I am in the excellent position of having extensive systems and optimization expertise - acquired during my Ph. D. studies -, as well as deep machine learning and AI knowledge from having spent the last 6+ years focusing on this research domain at IBM Research - Zurich. My research work has been put into production and applied to hundreds of IBM customers. Personal page at IBM: https://resedit.watson.ibm.com/researcher/edit.php?person=zurich-IGI Experience Jul 2015 - Present IBM Research Zurich, CH, Research Staff Member / Team Lead in AI/ML Nov 2012 - Jun 2015 IBM Research Zurich, CH, Postdoctoral Researcher in ML Explainability for classification and anomaly detection models with a focus on multivariate time and event series data Deep learning models (AEs, VAEs, LSTMs,
    [Show full text]
  • World-Class Performance with E-Aviation Charter Flights
    World-class performance with E-Aviation charter flights Fast, safe, flexible and economical travel to your business destination Enjoy the best personal service as you travel non-stop and directly to your business meetings. With our safe and flexible service, not a second of your time is wasted. No need to change aircraft, no unnecessary delays and no long check-in queues. These are the advantages for businesses whose travel needs reflect their status on the national and international stage. Compare the efficiency and cost savings of an E-Aviation charter flight against scheduled flying: Stuttgart – Sibiu – Stuttgart. Scheduled flight from Stuttgart E-Aviation Business Charter Flight from Stuttgart Day 1: Day 1: Meeting Point Airport Terminal 6:15 a.m. Meeting Point GAT, Stuttgart 6:15 a.m. Departure 7:45 a.m. Stuttgart Departure 6:30 a.m. Stuttgart, nonstop Arrival 9:05 a.m. Vienna Arrival 9:40 a.m. Sibiu (EEST) Departure 10:20 a.m. Vienna Arrival at office 10:00 a.m. Arrival 12:50 p.m. Bucharest (EEST) Meeting 10:00 – 16:00 h Journey with rental car 3:30 h Return journey from office 04:30 p.m. Arrival at Office 04:20 p.m. Sibiu Departure from Sibiu Airport 05:00 p.m. nonstop (EEST) Day 2: Arrival GAT 06:00 p.m. Stuttgart (MEZ) Meeting 9:00 – 14:00 h At Home 06:30 p.m. local Day 3: Return journey from office 01:45 p.m. Sibiu Costs: Journey with rental car 3:30 h Jet charter for 6 Persons Euro 10.600,00 Arrival Airport 05:15 p.m.
    [Show full text]
  • Roma As Alien Music and Identity of the Roma in Romania
    Roma as Alien Music and Identity of the Roma in Romania A thesis submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy 2018 Roderick Charles Lawford DECLARATION This work has not been submitted in substance for any other degree or award at this or any other university or place of learning, nor is being submitted concurrently in candidature for any degree or other award. Signed ………………………………………… Date ………………………… STATEMENT 1 This thesis is being submitted in partial fulfilment of the requirements for the degree of PhD. Signed ………………………………………… Date ………………………… STATEMENT 2 This thesis is the result of my own independent work/investigation, except where otherwise stated, and the thesis has not been edited by a third party beyond what is permitted by Cardiff University’s Policy on the Use of Third Party Editors by Research Degree Students. Other sources are acknowledged by explicit references. The views expressed are my own. Signed ………………………………………… Date ………………………… STATEMENT 3 I hereby give consent for my thesis, if accepted, to be available online in the University’s Open Access repository and for inter-library loan, and for the title and summary to be made available to outside organisations. Signed ………………………………………… Date ………………………… ii To Sue Lawford and In Memory of Marion Ethel Lawford (1924-1977) and Charles Alfred Lawford (1925-2010) iii Table of Contents List of Figures vi List of Plates vii List of Tables ix Conventions x Acknowledgements xii Abstract xiii Introduction 1 Chapter 1 - Theory and Method
    [Show full text]